In the preciding project I build a model with Charlie 🦀 to predict the price of a house, in this project we will deploy the model with FastAPI and Streamlit. FastAPI for other developers to use the model and Streamlit for the end-user.
.
├── backend # FastAPI
│ ├── config.py
│ ├── Dockerfile
│ ├── features
│ │ ├── build_features.py
│ │ ├── pipeline.py
│ │ └── transformers.py
│ ├── main.py
│ ├── models
│ │ └── catboost.pkl
│ ├── requirements.txt
│ ├── schemas
│ │ ├── address_schema.py
│ │ ├── property_schema.py
│ │ └── value_shema.py
│ └── utils.py
├── docker-compose.yml
├── frontend # streamlit
│ ├── app.py
│ ├── config.py
│ ├── Dockerfile
│ ├── images
│ ├── requirements.txt
│ └── utils.py
├── README.md
└── requirements.txt
It is a bit slow because it is hosted on a free server, but it works! Please be patient.
sudo docker compose up -d --build
View live demo: Immo Prediction Backend with FastAPI
View live demo: Immo Prediction Frontend with Streamlit
This project was done in 5 days including studying the theory and implementing the code.
This project was done as part of my AI trainee program at BeCode.
